Sky/river alike areas are important parts of natural scenes. The haze of these areas will impact the vividness of natural scenes. More serious, once the haze occurs in navigation of flight and ship, it will threaten the safety. However, previous researches did not dehaze well for these areas. In this paper, a new haze removal approach is presented to improve the dehazing effect for the sky/river alike areas based on our new discovers. Inspired by the two discovers, we define two theoretical clues: the external boundary clue and the internal clue. And then a correction model is constructed to correct the dark channel values in the sky/river alike areas. Finally, an optimization solution is presented to solve this model. Both the visual experiment and the quantitative experiment show that proposed method outperforms the classical dark channel method and the deep learning method in sky/river alike areas.

A new haze removal approach for sky/river alike scenes based on external and internal clues

The invention relates to a video monitoring system and method for target detection and tracking. The system comprises a video collection device, a target detection device, an information processing device, an information transmission device and an image collection device, wherein the image collection device is composed of a front end image collection device and a video decoding device and is usedfor obtaining a real-time video stream of a target object; the target detection device analyzes the position and the size of the target object in a video image; the information processing device performs real-time processing on related information of the target object within a continuous time to obtain a moving speed, a trajectory and direction information of the target object, and judges the number, the location and the size information of the target object in advance; the information transmission device sends the related information of the target object to the image collection device; and the image collection device controls the target object to always locate in a middle area of the screen and tracks the target object in real time. According to the video monitoring system and method, thetracking of numerous small targets is facilitated according to the deep neural network with multiple features, the recognition rate is improved, and it is beneficial to the good running of the tracking algorithm, and real-time tracking of the target object is achieved.

Video monitoring system and method for target detection and tracking

The invention provides an object tracking method and an object tracking system based on local classification The object tracking method comprises the steps of acquiring a positive sample and a negative sample based on a first frame, and dividing into a training sample set and a verification sample set; performing local block sampling on the training sample set and training a local classifier of the object and acquiring an objective model; utilizing a candidate in subsequent frame pictures based on a particle filtering frame, performing estimation on each candidate by means of a local block and a corresponding local classifier, and updating the object position to the position of the candidate with maximal confidence Therefore the invention provides the object tracking method and the object tracking system which overcome a drift tracking problem in a blocked condition, wherein the drift tracking problem cannot be settled based on global object tracking Furthermore the invention provides a sample updating mode based on double-threshold restriction Furthermore the weight of the local classifier is updated based on time domain stability of the object Partial shielding can be effectively determined and suppressed, and furthermore randomness and contingency are prevented

Object tracking method and object tracking system based on local classification

The invention discloses a single-image defogging method based on a ResNet neural network. The method realizes end-to-end defogging clearness processing on a foggy image, and includes the steps of: acquiring foggy images under rain/fog weather conditions and clear images of sunny days under the same scene to form an image data set; using SIFT to carry out key-point matching on the image data set, and correcting pixel offset of the images; building the ResNet-based deep neural network of which input is the single foggy scene image; and using the built deep neural network to realize end-to-end defogging clearness processing on the image with reduced quality. The method uses the ResNet network, can better extract features of the input image, has a very good defogging clearness effect on the foggy image under the specific scene, and is excellent in an image visual-restoration effect.

Single-image defogging method based on ResNet neural network

Recently, convolutional neural network (CNN) based on the encoder-decoder structure have been successfully applied to image dehazing. However, these CNN based dehazing methods have two limitations: First, these dehazing models are large in size with enormous parameters, which not only consumes much GPU memory, but also is hard to train from scratch. Second, these models, which ignore the structural information at different resolutions of intermediate layers, cannot capture informative texture and edge information for dehazing by stacking more layers. In this paper, we propose a light-weight end-to-end network named the residual dense pyramid network (RDPN) to address the above problems. To exploit the structural information at different resolutions of intermediate layers fully, a new residual dense pyramid (RDP) is proposed as a building block. By introducing a dense information fusion layer and the residual learning module, the RDP can maximize the information flow and extract local features. Furthermore, the RDP further learns the structural information from intermediate layers via a multiscale pyramid fusion mechanism. To reduce the number of network parameters and to ease the training process, we use one RDP in the encoder and two RDPs in the decoder, following a multilevel pyramid pooling layer for incorporating global context features before estimating the final result. The extensive experimental results on a synthetic dataset and real-world images demonstrate that the new RDPN achieves favourable performance compared with some state-of-the-art methods, e.g., the recent densely connected pyramid dehazing network, the all-in-one dehazing network, the enhanced pix2pix dehazing network, pixel-based alpha blending, artificial multi-exposure image fusions and the genetic programming estimator, in terms of accuracy, run time and number of parameters. To be specific, RDPN outperforms all of the above methods in terms of PSNR by at least 4.25 dB. The run time of the proposed method is 0.021 s, and the number of parameters is 1,534,799, only 6% of that used by the densely connected pyramid dehazing network.

A Novel Residual Dense Pyramid Network for Image Dehazing

The invention discloses a characteristic learning-based single image defogging method. Firstly, multi-scale extraction of texture structure characteristics of a foggy image is performed via a sparse autoencoder, and at the same time, various color characteristics related with fog are extracted. Then, a multi-layer neural network is adopted for sample training, the mapping relationship between the texture structure characteristics and the color characteristics and the scene depth in the foggy condition is learned, and a scene depth chart of the foggy image is estimated. On this basis, a transmittance chart is approximately estimated by using the scene depth chart. The transmittance chart effectively reflects the fog concentration of each local area in the foggy image. Finally, with the combination of an atmospheric scattering model, restoration is further carried out according to the transmittance chart and a fogless image is obtained. The invention allows restoration of foggy images so as to obtain high-quality fogless images. In addition, compared with conventional defogging methods, the characteristic learning-based single image defogging method of the invention achieves better universal scene adaptability.

Characteristic learning-based single image defogging method

The invention discloses a target tracking method based on local feature learning. A target object and background are resolved into a large number of local units having dimension and shape invariance which serve as training samples of a target and background classification model, and a mode of deep learning is adopted to learn local expression of the target object and background from the training samples. The degree of confidence that each specific area in an image belongs to the target object is judged, thereby realizing accurate positioning of the target object. Since local expression obtained by learning of a large number of samples has a high target identification capability, the tracking method has relatively high adaptability to situations such as target deformation and target occlusion. When an object appearance model is updated, local areas with a relatively high target confidence degree are extracted as training samples of the model, and the model is updated. In the process of target tracking, the method continuously updates the appearance model, learns key characteristics of the target object, and can obtain a relatively good tracking effect under the circumstance that an appearance change is relatively big.

Target tracking method based on local feature learning

A deep learning-based single image deraining algorithm is proposed in this work. Instead of modeling a rain layer as a linear function between the rain image and its clear version as previous works do, we directly formulate the clear image as the result of a non-linear mapping of thrain image. We construct a coarse deraining convolutional network and a refinement convolutional network to learn this non-linear mapping function. The coarse deraining network is trained to detect the rain streaks with different directions, and restore a raw derained result. The refinement network aims at refining the result according to the raw derained image and the original rain image. By combining the two networks, we are able to well-restore the rain-free image. Experimental results demonstrate that the proposed deraining method can produce high-quality clear images from both synthetic and real-world rain images, outperforming the state-of-the-art methods qualitatively and quantitatively.

Single image deraining using deep convolutional networks

Traditional dehazing techniques, as a well-studied topic in image processing, are now widely used to eliminate the haze effects from individual images. However, even the state-of-the-art dehazing algorithms may not provide sufficient support to video analytics, as a crucial pre-processing step for video-based decision making systems (e.g., robot navigation), due to the limitations of these algorithms on poor result coherence and low processing efficiency. This paper presents a new framework, particularly designed for video dehazing, to output coherent results in real time, with two novel techniques. We decompose the dehazing algorithms into three generic components, namely transmission map estimator, atmospheric light estimator and haze-free image generator. They can be simultaneously processed by multiple threads in the distributed system, such that the processing efficiency is optimized by automatic CPU resource allocation based on the workloads. The combination of these techniques enables our framework to generate highly consistent and accurate dehazing results in real-time, by using only 3 PCs connected by Ethernet.

Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing

Traditional dehazing techniques, as a well studied topic in image processing, are now widely used to eliminate the haze effects from individual images. However, the state-of-the-art dehazing algorithms may not provide sufficient support to video analytics, as a crucial pre-processing step for video-based decision making systems (e.g., robot navigation), due to poor coherence and low processing efficiency of the present algorithms. This paper presents a new framework, particularly designed for video dehazing, to output coherent results in real time, with two novel techniques. Firstly, we decompose the dehazing algorithms into three generic components, namely transmission map estimator, atmospheric light estimator and haze-free image generator. They can be simultaneously processed by multiple threads in the distributed system, such that the processing efficiency is optimized by automatic CPU resource allocation based on the workloads. Secondly, a cross-frame normalization scheme is proposed to enhance the coherence among consecutive frames, by sharing the parameters of atmospheric light from consecutive frames in the distributed computation platform. The combination of the above three components enables our framework to generate highly consistent and accurate dehazing results in real-time, by using only 5 PCs connected by Ethernet.

A component-driven distributed framework for real-time video dehazing

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